MétaCan
Menu
Back to cohort
Record W2911085615 · doi:10.1051/epjconf/202023915004

International network of nuclear structure and decay data evaluators

2020· article· en· W2911085615 on OpenAlexaff
P. Dimitriou, S. Basunia, L. A. Bernstein, Jun Chen, Z. Elekes, Xiao‐Long Huang, A. M. Hurst, H. Iimura, Ashok Jain, J. H. Kelley, T. Kibédi, F. G. Kondev, S. Lalkovski, E. A. McCutchan, I. A. Mitropolsky, G. Mukherjee, A. Negreţ, C. D. Nesaraja, N. Nica, Sorin Pascu, Alexander Rodionov, Balraj Singh, Sukhjeet Singh, Michael C. Smith, A. A. Sonzogni, J. Timár, J.K. Tuli, M. Verpelli, Dong Yang, Viktor Zerkin

Bibliographic record

VenueEPJ Web of Conferences · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomical and nuclear sciences
Canadian institutionsMcMaster University
FundersLawrence Berkeley National LaboratoryOak Ridge National LaboratoryNuclear PhysicsOffice of ScienceNorth Carolina State UniversityU.S. Department of Energy
KeywordsNuclear dataAtomic energyAgency (philosophy)Nuclear structureData setComputer scienceData scienceNuclear physicsPhysicsSociologyArtificial intelligence

Abstract

fetched live from OpenAlex

Compilation, evaluation and dissemination of nuclear data are arduous tasks that rely on contributions from experts in both the basic and applied sciences communities whose efforts are coordinated by the International Atomic Energy Agency (IAEA). The Evaluated Nuclear Structure Data File (ENSDF) includes the most extensive and comprehensive set of nuclear structure and decay data evaluations performed by the international network of Nuclear Structure and Decay Data evaluators (NSDD) under the auspices of the IAEA. In this report we describe the recent NSDD activities supported by the IAEA and provide some future perspectives.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.083
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.083
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.069
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0170.013
Science and technology studies0.0030.001
Scholarly communication0.0050.005
Open science0.0040.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0390.023

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.264
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEPJ Web of ConferencesSame topicAstronomical and nuclear sciencesFrench-language works237,207